Abstract
A hybrid technique combining the AdaBoost ensemble method with the neural network with fuzzy membership function (NEWFM) method is proposed for medical data classification and disease diagnosis. Combining the Adaboost, a general method used to improve the performance of learning methods, with the ‘standard’ NEWFM, which uses as base classifiers, ensures better accuracy in medical data classification tasks and diagnosis of diseases. To validate the proposal, four medical datasets related to epileptic seizure detection, Parkinson, cardiovascular (heart), and hepatitis disease diagnoses were used. The results show an average classification accuracy of 95.8% (made up of best accuracy of 99.5% for epileptic seizure, 87.9% for Parkinson, 97.4% for cardiovascular (heart) disease, and 98.7% for Hepatitis dataset classifications), which suggests that the proposed technique is capable of efficient medical data classification and potential applications in disease diagnosis and treatment.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
